{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Using downloaded and verified file: ../../Datasets/FashionMNIST\\FashionMNIST\\raw\\train-images-idx3-ubyte.gz\nExtracting ../../Datasets/FashionMNIST\\FashionMNIST\\raw\\train-images-idx3-ubyte.gz to ../../Datasets/FashionMNIST\\FashionMNIST\\raw\nUsing downloaded and verified file: ../../Datasets/FashionMNIST\\FashionMNIST\\raw\\train-labels-idx1-ubyte.gz\nExtracting ../../Datasets/FashionMNIST\\FashionMNIST\\raw\\train-labels-idx1-ubyte.gz to ../../Datasets/FashionMNIST\\FashionMNIST\\raw\nUsing downloaded and verified file: ../../Datasets/FashionMNIST\\FashionMNIST\\raw\\t10k-images-idx3-ubyte.gz\nExtracting ../../Datasets/FashionMNIST\\FashionMNIST\\raw\\t10k-images-idx3-ubyte.gz to ../../Datasets/FashionMNIST\\FashionMNIST\\raw\nUsing downloaded and verified file: ../../Datasets/FashionMNIST\\FashionMNIST\\raw\\t10k-labels-idx1-ubyte.gz\nExtracting ../../Datasets/FashionMNIST\\FashionMNIST\\raw\\t10k-labels-idx1-ubyte.gz to ../../Datasets/FashionMNIST\\FashionMNIST\\raw\nProcessing...\nDone!\n<class 'torchvision.datasets.mnist.FashionMNIST'>\n60000 10000\ntorch.Size([1, 28, 28]) 9\n"
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import matplotlib.pyplot as plt \n",
    "import time \n",
    "import sys\n",
    "\n",
    "mnist_train = torchvision.datasets.FashionMNIST(root=\"../../Datasets/FashionMNIST\",train=True,download=True,transform=transforms.ToTensor())\n",
    "mnist_test = torchvision.datasets.FashionMNIST(root=\"../../Datasets/FashionMNIST\",train=False,download=True,transform=transforms.ToTensor())\n",
    "\n",
    "print(type(mnist_train))\n",
    "print(len(mnist_train),len(mnist_test))\n",
    "\n",
    "# 访问样本数据\n",
    "feature,label = mnist_train[0]\n",
    "print(feature.shape,label) # C x H x W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "def get_fashion_mnist_labels(labels):\n",
    "    text_labels = ['t-shirt','trouser','pullover','dress','coat','sandal','shirt','sneaker','bag','ankel boot']\n",
    "\n",
    "    return [text_labels[int(i)] for i in labels]\n",
    "\n",
    "def show_fashion_mnist(images,labels):\n",
    "\n",
    "    _,figs = plt.subplots(1,10,figsize=(12,12))\n",
    "    for f,img,lbl in zip(figs,images,labels):\n",
    "        img = img.view((28,28)).numpy()\n",
    "        f.imshow(img)\n",
    "        f.set_title(lbl)\n",
    "        f.axes.get_xaxis().set_visible(False)\n",
    "        f.axes.get_yaxis().set_visible(False)\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "X,y = [],[]\n",
    "for i in range(10):\n",
    "    X.append(mnist_train[i][0])\n",
    "    y.append(mnist_train[i][1])\n",
    "\n",
    "show_fashion_mnist(X,get_fashion_mnist_labels(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "1.59\n"
    }
   ],
   "source": [
    "batch_size = 64\n",
    "num_workers = 4\n",
    "\n",
    "train_iter = torch.utils.data.DataLoader(mnist_train,batch_size=batch_size,shuffle=True,num_workers=num_workers)\n",
    "test_iter = torch.utils.data.DataLoader(mnist_test,batch_size=batch_size,shuffle=False,num_workers=num_workers)\n",
    "\n",
    "start = time.time()\n",
    "for X,y in train_iter:\n",
    "    continue\n",
    "\n",
    "print(\"{:.2f}\".format(time.time() - start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}